1.1 Linguistic Inquiry and Word Coun

Word classification used the Linguistic Inquiry and Word Count 2015 (LIWC) dictionary (Pennebaker et al., 2015) and tokenising tools from the Quanteda R package (Benoit, 2018). Word counts and words-per-sentence were calculated using stringi (Gagolewski, 2018), a wrapper to the ICU regex library.

Following Yu (2014) drawing on Newman, Groom, Handelman, & Pennebaker (2008) we used the following LIWC categories:

• All Pronouns (pronoun)
• First person singular pronouns (i)
• First person plural pronouns (we)
• Verbs (verb)
• Auxiliary verbs (auxverb)
• Social processes (social)
• Positive emotions (posemo)
• Negative emotions (negemo)
• Tentative words (tentat)
• Articles (article)
• Prepositions (preps)
• Anger words (anger)
• Swear words (swear)
• Cognitive processes (cogproc)
• Words longer than six letters (Sixltr)

We also included mean words-per-sentence (WPS), total speach word count (WC) and Flesch–Kincaid grade level (FK) (Kincaid, Fishburne, Rogers, & Chissom, 1975), calculated using Quanteda (Benoit, 2018) and stringi (Gagolewski, 2018).

1.2 Women vs Men

There are no categories where gender differences meet the effect size threshold of \(|0.2|\) suggested by Cohen (1988, pp. 25–26) to indicate a small effect. 4 categories – words with more than six letters, prepositions, words-per-sentence and Flesh-Kincaid grade level – exceeded the \(|0.1|\) threshold suggested by Newman et al (2008).

1.3 Short lists vs Non-Short lists

The following plots show changes in the occurences of selected LIWC terms, words-per-sentence, total word count and Flesch–Kincaid grade level, over the course of an MP’s career. There do not appear to be any notable changes in speaking style over the course of female Labour MPs’ careers.

Occurence of selected LIWC terms

There are no categories among female Labour MPs by selection process meeting the \(|0.2|\) threshold. Only one category – first person plural pronouns, d=0.19 – exceeded \(|0.1|\).

1.4 Conservatives vs Labour

There are no categories with effect sizes exceeding \(|0.2|\) between Labour and Conservative MPs, like inter-Labour differences.

1.5 All MPs Gender Differences

There are no categories with effect sizes exceeding \(|0.2|\) when comparing all male and female MPs elected from 1997 onwards. There is only one category, “Articles”, with an effect size of 0.11, greater than the \(|0.1|\) threshold suggested by Newman et al. (2008).

1.6 POS Analysis

Part-of-speech (POS) tagging was done using spaCy (Honnibal & Montani, 2017) and the spacyr package (Benoit & Matsuo, 2018). There is one small gender difference (d = \(|0.22|\)) in the use of plural nouns, which make up 5.85% of the words used by female Labour MPs, compared to 5.03% of words spoken by male Labour MPs. As with LIWC, there are no categories where d >= \(|0.2|\) when comparing female Labour MPs by selection process.

1.7 Tokenising / Keyness

The most commonly used words by both men and women would be protocol decorum expressions, so we calculate the keyness of words to identify gender differences in the choices of topics raised by men and women, and by short-list and non-short list women.

1.7.1 Men vs Women

Keyness – a linguistic measure of the frequency of different words in two groups of texts – reveals clear gender differences in the most disproportionately common words used by female and male Labour MPs. Unsurprisingly, despite male MPs saying almost twice as many words (30,596,921 vs 15,897,929) as their female colleagues, female Labour MPs were more than two-and-a-half (2.61) times as likely to say “women”. They were also much more likely to use “women’s” and “woman” in parliamentary debate. Female Labour MPs also appear much more likely to discuss “children”, “people”, “care”, “families”, “home”, “parents”, “work” and social policy areas such as “services”, “disabled [people]” and “housing” than their male colleagues. Male MPs were more likely to refer to military topics (“Iraq”, “nuclear”), and to parliamentary process and protocol – “question”, “political”, “conservative”, “electoral”, “house”, “party”, “argument” “liberal” and “point” are far more common in speeches by male Labour MPs than by female ones. This could suggest that male Labour MPs are more comfortable using the traditional language of House of Commons debate, and are more concerned with the rules, procedures and processes of the parliamentary system than their female colleagues.

Keyness between Labour MPs, by Gender

1.7.2 Short lists vs Non-Short lists

Keyness differences by selection process are not as obviously stereotypical. Nonetheless, the most common words amongst AWS MPs included “carers”, “disabled”, “bedroom” and “sen”². Also of note is AWS MPs making more references to their “constituency” and its “constituents”, suggesting that AWS MPs may draw on the fact they were elected by their constituents as a source political legitimacy, at least more than non-AWS MPs.

Keyness between Female Labour MPs, by Selection Process

1.7.3 Labour vs Conservative

The keyness differences between Labour and Conservative MPs are much greater than gender differences within Labour. The very high use of “Lady” by Conservative MPs is reflective of the greater proportion of female MPs in other parties, as it is often used to refer to comments by other members of the house. It may also represent a greater use of traditional hosue decorum by Conservative MPs.

Keyness between Labour and Conservative MPs

1.8 Bigrams

We created bigrams of all first person plural and singular pronouns for female Labour MPs. As above, AWS MPs are far more likely to make references to their constituency or their constituents.

Bigram Keyness in Female Labour MPs by Selection Process

1.9 Naive Bayes classification

We trained a Naive Bayes classifier with document-frequency priors and a multinomial distribution to predict the gender of speakers when given speeches by all Labour MPs in our dataset, and the selection process when only given female Labour MPs. The accuracy of both models were roughly equivalent, 70.55% accuracy when predicting gender and 70.84% when predicting short lists. By contrast, the classifier could distinguish between Labour and Conservative speeches with 74.23% accuracy.

1.10 Topic Models

Using topic models to classify text is widely used in social sciences (Grimmer & Stewart, 2013), as, when combined with the large volume of plain text data available, it allows for a rapid and consistent method of analysis . Topic modelling and other statistic methods of textual analysis are not a substitute for reading the texts themselves, but can augment other analysis or – as in this case – analyse and classify larger amounts of text than would be feasible using human coders (Grimmer & Stewart, 2013). Topic models classify a series of documents (in this case individual speeches) into one of a given number of topics, identifying terms that are common in some documents but rare in others. When developing topic models, there is a trade-off between high precision in the classification of each document with broader topics when using smaller numbers of topics, or lower precision in individual speech classification with more finely-grained topics when using larger numbers of topics. Grimmer & Stewart (2013) also highlight the importance of validating unsurpervised topic models when applied to new sets of texts.

The R package stm (Roberts, Stewart, & Tingley, 2018) implements a structured topic model (STM) (Arora et al., 2013; Roberts, Stewart, & Airoldi, 2016). An STM incorporates data about the writer or speaker into the topic classification algorithm. This differs from traditional topic modelling methods using latent variables to identify topics (e.g. with latent Dirichlet allocation Blei, Ng, & Jordan, 2003), and then comparing proportions of each topic to one or more external variables. STM allows us to incorporate the variables we are interested in to the topic model itself, i.e. the proportion of speechs classified as belonging to each topic can vary as a function of the AWS variable.

We incorporated the AWS status of speakers into our topic model, using all speeches by female Labour MPs, with their AWS status as a covariate in classifying topics. We then matched these topics to speechs by male Labour MPs.

We produced two different structured topic model implementations, with different numbers of topics (K). For each implementation, we created Fruchterman-Reingold (Fruchterman & Reingold, 1991) diagrams. Larger vertices indicate more common topics, and the plot uses a colour scale to indicate the proportion of speeches classed in that topic made by AWS and non-AWS female Labour MPs. The space between vertices indicate the closeness of two topics.

1.10.1 Short lists vs Non-Short lists - K69

The first implementation used an algorithm developed by Lee & Mimno (2014), implemented in the stm package (Roberts et al., 2018), to estimate the number of topics across all speeches made by female Labour MPs, using the “spectral” method developed by Arora et al. (2013), implemented by Roberts et al. (2018). The resulting topic model has 69 topics, across 81,607 documents and a dictionary of 115,477 words. However, the topic quality with K = 69 is poor, and several topics have poor semantic coherence (see ).

There are several clusters of topics in . For instance, we can see the closeness of Topic 15 (economics and government budgets) and Topic 43 (housing), as both include discussions of budgets and costs, while Topics 23 (bill clauses and admendments) and 16 (education) are very far apart.

Fruchterman-Reingold plot of K69 Network

Coherence of K69 Topic Models

Count and Distribution of Topics – K69

Topic Number	AWS Speeches	Percent of AWS Speeches	Non-AWS Speeches	Percent of non-AWS Speeches	Male MP Speeches	Percent of Male MP Speeches
Topic 1	1,272	2.37%	353	1.27%	3,434	2.03%
Topic 2	334	0.62%	127	0.46%	1,091	0.64%
Topic 3	241	0.45%	71	0.25%	427	0.25%
Topic 4	550	1.02%	133	0.48%	835	0.49%
Topic 5	826	1.54%	206	0.74%	2,452	1.45%
Topic 6	978	1.82%	915	3.28%	4,060	2.4%
Topic 7	648	1.21%	236	0.85%	1,770	1.05%
Topic 8	70	0.13%	25	0.09%	125	0.07%
Topic 9	265	0.49%	309	1.11%	862	0.51%
Topic 10	1,024	1.91%	513	1.84%	1,065	0.63%
Topic 11	940	1.75%	580	2.08%	3,793	2.24%
Topic 12	313	0.58%	319	1.14%	1,309	0.77%
Topic 13	325	0.61%	146	0.52%	1,181	0.7%
Topic 14	1,596	2.97%	461	1.65%	2,885	1.7%
Topic 15	1,386	2.58%	642	2.3%	4,686	2.77%
Topic 16	1,407	2.62%	525	1.88%	3,651	2.16%
Topic 17	3,690	6.87%	1,459	5.23%	19,358	11.43%
Topic 18	1,026	1.91%	847	3.04%	4,760	2.81%
Topic 19	640	1.19%	423	1.52%	2,130	1.26%
Topic 20	872	1.62%	216	0.77%	2,262	1.34%
Topic 21	658	1.23%	363	1.3%	914	0.54%
Topic 22	818	1.52%	439	1.57%	1,965	1.16%
Topic 23	795	1.48%	518	1.86%	3,553	2.1%
Topic 24	385	0.72%	199	0.71%	1,079	0.64%
Topic 25	240	0.45%	74	0.27%	422	0.25%
Topic 26	788	1.47%	200	0.72%	1,738	1.03%
Topic 27	266	0.5%	120	0.43%	1,010	0.6%
Topic 28	847	1.58%	350	1.25%	3,135	1.85%
Topic 29	1,110	2.07%	327	1.17%	944	0.56%
Topic 30	1,132	2.11%	462	1.66%	6,444	3.81%
Topic 31	996	1.85%	975	3.49%	6,078	3.59%
Topic 32	76	0.14%	64	0.23%	335	0.2%
Topic 33	1,238	2.31%	985	3.53%	6,613	3.9%
Topic 34	1,124	2.09%	521	1.87%	3,335	1.97%
Topic 35	650	1.21%	657	2.35%	2,294	1.35%
Topic 36	601	1.12%	154	0.55%	548	0.32%
Topic 37	455	0.85%	194	0.7%	1,554	0.92%
Topic 38	1,246	2.32%	991	3.55%	2,849	1.68%
Topic 39	1,917	3.57%	936	3.35%	7,664	4.53%
Topic 40	848	1.58%	290	1.04%	2,419	1.43%
Topic 41	63	0.12%	40	0.14%	204	0.12%
Topic 42	853	1.59%	590	2.11%	2,016	1.19%
Topic 43	1,344	2.5%	604	2.16%	2,266	1.34%
Topic 44	814	1.52%	288	1.03%	3,005	1.77%
Topic 45	602	1.12%	474	1.7%	1,086	0.64%
Topic 46	709	1.32%	150	0.54%	1,646	0.97%
Topic 47	664	1.24%	245	0.88%	2,992	1.77%
Topic 48	940	1.75%	901	3.23%	3,045	1.8%
Topic 49	835	1.55%	563	2.02%	2,537	1.5%
Topic 50	1,328	2.47%	1,219	4.37%	3,421	2.02%
Topic 51	1,076	2%	323	1.16%	2,453	1.45%
Topic 52	196	0.36%	85	0.3%	758	0.45%
Topic 53	590	1.1%	293	1.05%	746	0.44%
Topic 54	1,057	1.97%	824	2.95%	5,570	3.29%
Topic 55	302	0.56%	157	0.56%	868	0.51%
Topic 56	535	1%	398	1.43%	847	0.5%
Topic 57	656	1.22%	314	1.13%	1,990	1.18%
Topic 58	468	0.87%	182	0.65%	1,125	0.66%
Topic 59	426	0.79%	183	0.66%	700	0.41%
Topic 60	562	1.05%	297	1.06%	1,389	0.82%
Topic 61	86	0.16%	28	0.1%	174	0.1%
Topic 62	550	1.02%	343	1.23%	746	0.44%
Topic 63	690	1.28%	252	0.9%	1,726	1.02%
Topic 64	594	1.11%	244	0.87%	2,247	1.33%
Topic 65	662	1.23%	457	1.64%	907	0.54%
Topic 66	1,493	2.78%	527	1.89%	4,073	2.41%
Topic 67	737	1.37%	451	1.62%	3,237	1.91%
Topic 68	279	0.52%	145	0.52%	547	0.32%
Topic 69	1	0%	NA	NA%	NA	NA%

K69 Pyramid Chart

K69 Bar Chart

1.10.1.1 Word Occurences

The table below shows the ten most common words in each topic, and the ten words with the highest FREX score, a measure that uses a harmonic mean of word exclusivity and topic coherence (Airoldi & Bischof, 2016).

Topic Number	Top Ten Words	Top Ten FREX
Topic 1	secretary, state, tell, ministers, given, today, department, can, confirm, said	secretary, state, confirm, tell, ministers, state’s, minister’s, explain, please, discussions
Topic 2	safety, register, registration, indicated, registered, electoral, risk, risks, number, individual	registration, indicated, hse, canvass, register, gurkhas, safety, dissent, hare, trustee
Topic 3	make, sure, statement, progress, difference, northern, ireland, towards, representations, responsibilities	statement, make, sure, progress, ireland, representations, difference, northern, milton, departmental
Topic 4	debt, water, credit, charges, pay, loan, loans, people, financial, cost	payday, loan, lenders, debts, loans, debt, charges, water, high-cost, creditors
Topic 5	house, committee, parliament, leader, select, motion, parliamentary, debate, scrutiny, business	select, leader, house, motion, committee, backbench, scrutiny, committees, benchers, parliamentary
Topic 6	new, development, work, need, investment, strategy, must, programme, working, also	development, strategy, develop, project, regional, projects, partnership, together, developed, build
Topic 7	road, petition, residents, car, vehicles, petitioners, dogs, roads, site, house	petitioners, lap-dancing, petition, dogs, dog, pedestrians, cycling, declares, drivers, accidents
Topic 8	important, agree, welcome, country, making, particularly, thank, part, makes, good	agree, welcome, important, absolutely, makes, making, friend’s, thank, particularly, giving
Topic 9	companies, market, company, competition, energy, consumers, prices, price, consumer, customers	competition, companies, market, wholesale, suppliers, company, regulator, ofgem, supplier, consumers
Topic 10	women, men, equality, women’s, discrimination, rights, gender, equal, woman, marriage	gender, bishops, transgender, women’s, women, abortion, same-sex, marriage, equality, gay
Topic 11	energy, climate, fuel, change, green, carbon, emissions, gas, environmental, industry	renewables, solar, insulation, feed-in, biofuels, greenhouse, dioxide, kyoto, carbon, climate
Topic 12	office, post, offices, royal, service, closure, mail, services, network, christmas	offices, mail, sub-post, post, sub-postmasters, closures, consignia, swindon, closure, office
Topic 13	mr, north, south, east, west, spoke, friends, birmingham, talked, central	ealing, spoke, dorset, lothian, ayrshire, glasgow, chris, southwark, pontefract, birmingham
Topic 14	pension, scheme, benefit, pensions, benefits, pensioners, system, credit, allowance, income	pension, esa, pensions, claimants, retirement, pip, pensioners, incapacity, dwp, means-testing
Topic 15	economy, jobs, economic, growth, unemployment, country, investment, chancellor, budget, crisis	unemployment, recession, growth, economy, obr, deficit, inflation, economic, forecast, borrowing
Topic 16	schools, school, education, children, teachers, parents, pupils, educational, special, primary	academies, pupil, grammar, schools, pupils, teachers, ofsted, school, teacher, sen
Topic 17	want, say, one, think, know, need, us, get, go, see	think, say, things, want, something, saying, going, lot, really, go
Topic 18	review, report, commission, independent, process, recommendations, inquiry, also, system, standards	recommendations, inquiry, panel, audit, independent, recommendation, reviews, fsa, complaints, review
Topic 19	business, businesses, small, financial, bank, banks, insurance, rates, industry, enterprise	smes, medium-sized, businesses, bank, enterprises, enterprise, banking, rbs, business, rock
Topic 20	wales, industry, welsh, north-east, england, assembly, constituency, jobs, manufacturing, uk	welsh, wales, steel, cardiff, north-east, assembly, visteon, newcastle, manufacturing, tyneside
Topic 21	care, services, social, mental, need, health, home, provision, service, older	mental, care, social, elderly, older, advocacy, services, residential, palliative, discharges
Topic 22	pay, work, workers, employment, working, wage, minimum, employers, paid, national	wage, workers, zero-hours, employees, paternity, employer, minimum, employers, employment, workplace
Topic 23	amendment, clause, amendments, new, 1, lords, section, 2, act, clauses	amendment, nos, insert, subsection, clause, amendments, clauses, section, lords, schedule
Topic 24	report, last, since, said, received, published, year, following, official, end	march, vol, official, january, july, november, published, december, june, october
Topic 25	made, clear, impact, decision, changes, recent, assessment, decisions, effect, proposed	made, decision, assessment, clear, decisions, impact, implications, recent, changes, effect
Topic 26	funding, cuts, fund, cut, budget, grant, spending, bbc, review, flood	flood, funding, bbc, formula, grant, flooding, floods, cumbria, lottery, grants
Topic 27	money, spent, extra, spend, liberal, cost, spending, value, opposition, tory	money, spent, liberal, spend, democrats, tories, tory, lib, democrat, conservatives
Topic 28	constituency, great, community, proud, many, sport, one, also, world, new	maiden, arts, football, museum, museums, sport, olympic, games, sports, heritage
Topic 29	families, child, poverty, children, parents, work, credit, working, family, living	lone, poverty, childcare, families, low-income, child, nursery, four-year-olds, nurseries, joseph
Topic 30	party, conservative, vote, parliament, political, election, labour, parties, scottish, elected	party, vote, voting, conservative, party’s, voters, election, voted, votes, politics
Topic 31	point, can, may, issue, take, however, whether, matter, understand, consider	matter, point, understand, consider, certainly, accept, possible, issue, course, happy
Topic 32	member, said, lady, mentioned, raised, comments, speech, referred, points, remarks	member, lady, comments, remarks, bromley, interesting, chislehurst, pointed, front-bench, mentioned
Topic 33	european, uk, eu, countries, united, union, europe, states, british, trade	accession, enlargement, wto, lisbon, treaty, eu, doha, european, negotiations, brexit
Topic 34	education, skills, young, training, students, university, college, higher, science, apprenticeships	ema, fe, students, apprenticeship, universities, qualifications, apprenticeships, graduates, vocational, courses
Topic 35	local, authorities, authority, planning, community, communities, councils, area, guidance, system	authorities, local, authority, planning, councils, councillors, locally, guidance, localism, communities
Topic 36	disabled, carers, disability, support, disabilities, needs, caring, autism, learning, can	carers, autism, autistic, disabled, disabilities, disability, dementia, carer, caring, deaf
Topic 37	environment, marine, fishing, sea, industry, natural, fish, countryside, rural, fisheries	fishermen, cod, forestry, biodiversity, habitats, mmo, fishing, fish, cfp, fisheries
Topic 38	justice, court, violence, victims, cases, criminal, domestic, courts, prison, offence	attorney-general, defendants, defendant, prison, prosecutors, solicitor-general, stalking, prosecutor, prisons, prosecution
Topic 39	international, foreign, rights, human, peace, un, conflict, world, aid, war	israel, palestinian, israeli, gaza, sri, zimbabwe, iran, yemen, hamas, palestinians
Topic 40	day, family, never, told, families, life, happened, constituent, man, went	man, died, son, story, stories, hillsborough, tragedy, daughter, husband, angry
Topic 41	proposals, future, forward, consultation, plans, meet, paper, current, discuss, bring	proposals, consultation, paper, plans, forward, discuss, white, proposal, meet, implement
Topic 42	behaviour, crime, antisocial, alcohol, young, drugs, drug, problem, use, tackle	antisocial, asbos, alcohol, alcohol-related, binge, psychoactive, drinking, fireworks, behaviour, graffiti
Topic 43	housing, homes, social, affordable, private, home, accommodation, rent, need, properties	housing, tenants, rented, tenancies, homelessness, leasehold, landlords, rents, properties, leaseholders
Topic 44	question, order, mr, put, asked, answer, questions, ask, speaker, time	question, answer, questions, speaker, asked, deputy, answers, order, apologise, read
Topic 45	research, cancer, treatment, medical, condition, screening, disease, can, patients, use	embryos, prostate, cervical, hepatitis, cloning, transplant, embryo, fertilisation, embryonic, endometriosis
Topic 46	online, internet, farmers, animals, digital, animal, broadband, sites, tickets, technology	cull, badgers, badger, fur, bovine, mink, culling, circuses, touts, snares
Topic 47	defence, forces, armed, plymouth, personnel, service, military, army, nuclear, royal	mod, naval, hms, submarines, dockyard, veterans, armed, plymouth, covenant, personnel
Topic 48	information, home, security, data, immigration, control, orders, system, terrorism, appeal	extradition, tpims, sia, warrant, detention, checks, tpim, terrorism, intercept, identity
Topic 49	police, officers, crime, policing, home, force, service, forces, officer, chief	constable, constables, officers, policing, police, soca, ipcc, constabulary, pcsos, hmic
Topic 50	nhs, hospital, patients, health, services, hospitals, care, service, trust, trusts	dentists, dentistry, pharmacies, pct, nhs, hospitals, hospital, dental, trusts, patients
Topic 51	tax, budget, cut, chancellor, cuts, rate, income, vat, benefit, hit	50p, vat, millionaires, hit, tax, allowances, credits, richest, chancellor, ifs
Topic 52	years, now, two, time, first, three, past, one, months, ago	years, three, months, ago, two, past, weeks, five, four, now
Topic 53	staff, doctors, emergency, medical, service, training, nurses, royal, junior, ambulance	ambulance, junior, staffing, doctors, halifax, posts, nurses, fss, staff, cpr
Topic 54	bill, legislation, act, law, rights, provisions, powers, regulations, place, believe	bill, legislation, bill’s, provisions, passage, regulations, legislative, draft, statute, definition
Topic 55	public, sector, private, organisations, service, voluntary, services, society, community, organisation	public, voluntary, organisations, sector, private, co-operative, volunteering, volunteers, volunteer, co-operatives
Topic 56	health, national, inequalities, programme, suicide, disease, department, prevention, among, risk	flu, hiv, pandemic, inequalities, infections, suicide, mortality, infection, mrsa, vaccine
Topic 57	council, london, areas, city, area, constituency, centre, rural, county, liverpool	county, mayor, borough, cities, liverpool, city, regeneration, council’s, london, towns
Topic 58	advice, legal, cases, civil, hull, aid, case, compensation, claims, service	hull, tribunal, legal, compensation, solicitors, advice, concentrix, servants, lawyers, tribunals
Topic 59	people, work, many, young, get, people’s, can, help, lives, job	people, people’s, get, getting, work, young, jobcentre, lives, youth, find
Topic 60	tax, revenue, relief, duty, uk, avoidance, hmrc, charities, companies, taxation	evasion, hmrc, gaar, avoidance, inland, stamp, revenue, relief, gift, dependencies
Topic 61	government, government’s, policy, labour, previous, scotland, scottish, commitment, policies, coalition	government, previous, policy, government’s, scotland, coalition, scottish, labour, disappointing, administrations
Topic 62	trafficking, home, uk, asylum, refugees, immigration, country, human, migration, britain	trafficking, slavery, trafficked, sierra, leone, slave, dubs, fgm, yarl’s, wilberforce
Topic 63	food, products, industry, smoking, advertising, tobacco, ban, product, standards, shops	gambling, betting, sunbed, tobacco, cocoa, meat, supermarkets, labelling, retailers, packaging
Topic 64	members, debate, many, issues, also, today, heard, opportunity, hope, issue	members, debate, heard, speak, sides, issues, hear, opportunity, listened, pleased
Topic 65	children, child, parents, young, children’s, family, contact, vulnerable, adoption, abuse	csa, adopters, adoption, child’s, cafcass, looked-after, children’s, children, safeguarding, barred
Topic 66	transport, rail, bus, services, line, travel, train, network, passengers, london	rail, passengers, passenger, heathrow, hs2, freight, high-speed, crossrail, airlines, runway
Topic 67	year, million, number, increase, figures, increased, billion, 1, average, cost	million, figures, figure, increased, increase, compared, year, total, fallen, estimates
Topic 68	support, ensure, can, help, aware, taking, take, provide, action, continue	aware, ensure, support, taking, steps, continue, help, action, assure, encourage
Topic 69	deal, recently, new, can, lack, great, concern, done, move, given	deal, recently, lack, elsewhere, concern, great, improved, offered, done, new

1.10.2 Short lists vs Non-Short lists - K30

As seen in the word lists above, there is relatively scattershot semantic coherence, although exclusivity is high, when using the 69 topic models suggested by Lee and Mimno’s (2014) algorithm. We therefore re-ran the analysis, using 30 topic models, which has resulted in increased semantic coherence, albeit with slightly lower exclusivity, as illustrated in Figure .

Fruchterman-Reingold plot of K30 Network

Coherence of K30 Topic Models

Count and Distribution of Topics – K30

Topic Number	AWS Speeches	Percent of AWS Speeches	Non-AWS Speeches	Percent of non-AWS Speeches	Male MP Speeches	Percent of Male MP Speeches
Topic 1	1,792	3.34%	1,229	4.4%	8,163	4.82%
Topic 2	2,476	4.61%	2,514	9.01%	11,393	6.73%
Topic 3	1,082	2.01%	632	2.27%	926	0.55%
Topic 4	1,303	2.43%	900	3.23%	3,364	1.99%
Topic 5	1,976	3.68%	1,371	4.91%	9,653	5.7%
Topic 6	1,720	3.2%	623	2.23%	4,562	2.69%
Topic 7	2,721	5.07%	758	2.72%	4,045	2.39%
Topic 8	879	1.64%	381	1.37%	2,193	1.29%
Topic 9	1,008	1.88%	743	2.66%	1,747	1.03%
Topic 10	1,351	2.52%	658	2.36%	6,235	3.68%
Topic 11	2,144	3.99%	1,552	5.56%	4,494	2.65%
Topic 12	2,507	4.67%	883	3.16%	10,394	6.14%
Topic 13	1,231	2.29%	825	2.96%	3,972	2.35%
Topic 14	984	1.83%	646	2.32%	1,570	0.93%
Topic 15	1,180	2.2%	1,410	5.05%	4,935	2.91%
Topic 16	2,175	4.05%	1,302	4.67%	7,547	4.46%
Topic 17	5,309	9.89%	2,357	8.45%	25,255	14.91%
Topic 18	2,362	4.4%	1,003	3.59%	6,230	3.68%
Topic 19	1,183	2.2%	445	1.59%	3,305	1.95%
Topic 20	1,334	2.48%	561	2.01%	2,075	1.23%
Topic 21	4,361	8.12%	1,556	5.58%	11,845	6.99%
Topic 22	977	1.82%	359	1.29%	2,258	1.33%
Topic 23	1,787	3.33%	890	3.19%	6,124	3.62%
Topic 24	813	1.51%	233	0.84%	2,132	1.26%
Topic 25	1,604	2.99%	1,104	3.96%	4,917	2.9%
Topic 26	1,237	2.3%	664	2.38%	1,105	0.65%
Topic 27	668	1.24%	325	1.16%	1,796	1.06%
Topic 28	3,218	5.99%	1,001	3.59%	8,906	5.26%
Topic 29	1,121	2.09%	304	1.09%	4,463	2.64%
Topic 30	1,202	2.24%	673	2.41%	3,746	2.21%

K30 Pyramid Chart

K30 Bar Chart

1.10.2.1 Word Occurences

Words in topic

Topic Number	Top Ten Words	Top Ten FREX
Topic 1	bill, amendment, clause, new, legislation, amendments, act, committee, provisions, 1	amendment, clause, amendments, clauses, nos, insert, subsection, provisions, bill, tabled
Topic 2	issues, public, information, also, report, review, process, work, need, important	consultation, review, guidance, recommendations, information, considering, decisions, arrangements, framework, detailed
Topic 3	women, men, pay, equality, rights, women’s, discrimination, equal, work, woman	women, equality, gender, equalities, bishops, discrimination, female, women’s, equal, men
Topic 4	police, crime, officers, behaviour, policing, home, antisocial, community, work, force	policing, antisocial, constable, burglary, wardens, crime, constabulary, police, officers, pcsos
Topic 5	european, uk, countries, eu, union, trade, international, united, world, british	treaty, enlargement, wto, lisbon, doha, eu, eu’s, mod, multilateral, accession
Topic 6	transport, london, rail, bus, road, services, line, travel, network, train	rail, bus, passengers, fares, trains, buses, passenger, heathrow, congestion, hs2
Topic 7	people, work, benefit, pension, benefits, support, disabled, employment, carers, working	disabled, jobcentre, incapacity, carers, pension, claimants, esa, dla, pensions, atos
Topic 8	immigration, safety, uk, asylum, enforcement, home, number, illegal, licensing, animals	dogs, dog, id, visa, fur, mink, hse, sia, seekers, fireworks
Topic 9	health, research, cancer, treatment, medical, disease, can, smoking, patients, people	cancer, diseases, vaccine, flu, embryos, infections, diabetes, palliative, prostate, cervical
Topic 10	government, labour, conservative, party, opposition, policy, government’s, scotland, scottish, members	conservative, liberal, democrats, conservatives, scottish, democrat, scotland, tory, interruption, tories
Topic 11	care, health, nhs, services, service, hospital, patients, staff, trust, social	dentists, ambulance, dentistry, helier, dentist, nurses, hospital, pct, hospitals, dental
Topic 12	member, members, debate, house, mr, committee, said, time, speaker, north	member, speaker, mr, debate, spoke, thoughtful, backbench, debates, madam, select
Topic 13	companies, financial, company, market, scheme, money, debt, consumers, bank, credit	payday, annuity, oft, policyholders, penrose, fca, loan, prepayment, loans, annuities
Topic 14	young, people, health, mental, youth, prison, problems, drugs, alcohol, drug	prisons, probation, cannabis, reoffending, mental, prison, self-harm, youth, alcohol, sentences
Topic 15	cases, court, legal, law, case, justice, evidence, criminal, courts, home	judicial, attorney-general, defendant, extradition, tpims, suspects, court, courts, prosecution, isc
Topic 16	energy, businesses, business, jobs, investment, economy, industry, economic, new, sector	carbon, renewable, renewables, solar, low-carbon, energy, feed-in, manufacturing, steel, businesses
Topic 17	people, want, one, get, know, say, us, many, think, need	things, think, something, get, want, going, really, say, lot, go
Topic 18	education, schools, school, children, training, skills, parents, teachers, students, young	schools, teachers, pupils, curriculum, sen, academies, ofsted, pupil, grammar, attainment
Topic 19	constituency, city, people, many, years, work, centre, one, hull, great	fishermen, cod, hull, plymouth, maiden, fishing, fish, humber, fleetwood, tourism
Topic 20	housing, homes, people, private, london, social, home, affordable, need, accommodation	rent, tenants, landlords, rented, homelessness, homeless, rents, tenancies, housing, tenancy
Topic 21	tax, year, million, government, budget, cuts, cut, poverty, increase, billion	tax, obr, vat, millionaires, 50p, inflation, budget, fiscal, chancellor, cut
Topic 22	food, post, office, rural, petition, offices, farmers, royal, mail, government	petition, farmers, petitioners, meat, cull, labelling, cattle, badger, culling, beef
Topic 23	people, international, human, government, war, rights, country, un, conflict, world	syria, israel, civilians, palestinian, israeli, gaza, sri, holocaust, hatred, sierra
Topic 24	bbc, media, online, internet, sport, access, digital, culture, clubs, football	bbc, games, olympic, gambling, bbc’s, copyright, lap-dancing, broadband, radio, internet
Topic 25	local, authorities, funding, areas, services, council, community, authority, government, communities	local, authorities, funding, councils, grant, authority, formula, deprived, areas, partnership
Topic 26	children, child, families, care, family, parents, violence, support, domestic, victims	trafficked, csa, same-sex, adopters, child, rape, marriages, marriage, sexual, couples
Topic 27	planning, water, development, land, environment, site, sites, flood, environmental, area	forestry, biodiversity, masts, habitats, gypsy, flood, waterways, flooding, marine, mmo
Topic 28	secretary, state, house, last, statement, report, said, now, question, answer	secretary, statement, state, confirm, official, answer, vol, state’s, letter, written
Topic 29	parliament, wales, vote, commission, political, assembly, people, welsh, elected, charities	electoral, polling, gibraltar, voting, assembly, vote, votes, voter, ballot, elections
Topic 30	can, make, ensure, agree, important, take, made, point, sure, welcome	agree, aware, sure, ensure, taking, lady, welcome, steps, point, make

1.11 Discussion

There do not appear to be substantial or meaningful differences in the speaking styles of female Labour MPs selected through all women short lists when compared to their female colleagues selected through open short lists using LIWC. This is possibly due to the speaking style dominant in British parliamentary debate, which is more formal than the speech used in most day-to-day conversation. LIWC has American developers, and the dictionary may not be able to capture stylistic differences between American and British English, and may not include words commonly used in formal British English speech, limiting its usefulness in a British context.

There is more gender distinction in some selected terms and topics. AWS MPs are far more likely to make reference to their constituency and their constituents. In the debate between whether MPs should be “delegates” or “trustees” – the “mandate-independence controversy” outlined by Pitkin (1967) – the references to their constituents and constituencies suggests AWS MPs shy away from the Burkean concept of trusteeship and see themselves more as strict representatives of their constituents. In Andeweg & Thomassen’s (2005) typology of ex ante/ex post and above/below political representation, AWS MPs lean towards representation “from below”, although their selection process is ex ante/ex post.

AWS MPs refer to their constituents both specifically and in the abstract, particularly when criticising government policy. For example, in debate on 4th March 2015, Gemma Doyle, than the Labour MP for West Dunbartonshire (elected on an AWS in 2010), when asked if she would give way to Conservative MP Stephen Mosley, responded:

No, I will not [give way], because my constituents want me to make these points, not to give more time to Conservative Members.

On 2nd June 2010, during debate on Israel-Palestine, Valerie Vaz, MP for Walsall South:

My constituents want more than pressure. Will the Foreign Secretary come back to the House and report on a timetable for the discussions on a diplomatic solution, just as we did on Ireland?

On 4th April 2001, Betty Williams, member for Conwy from 1997–2010, raised the case of a wilderness guide in her constituency unable to access parts of the countryside due to foot and mouth disease:

Is my right hon. Friend aware that there is continuing concern about the limited access to the countryside and crags of north Wales? May I draw his attention to the circumstances of my constituent, Ric Potter? Like many others, he has had to travel to Scotland, where there is greater access. Will my right hon. Friend help us to enable people such as Ric Potter to find work in outdoor pursuits?

2.1 Full topic model summary - K69

## A topic model with 69 topics, 81607 documents and a 115477 word dictionary.

## Topic 1 Top Words:
##       Highest Prob: secretary, state, tell, ministers, given, today, department 
##       FREX: secretary, state, confirm, tell, ministers, state's, minister's 
##       Lift: dhar, lowell, qatada's, #nationalistsconfused, 1135, 2.5bn, 36,500 
##       Score: secretary, state, confirm, state's, tell, ministers, department 
## Topic 2 Top Words:
##       Highest Prob: safety, register, registration, indicated, registered, electoral, risk 
##       FREX: registration, indicated, hse, canvass, register, gurkhas, safety 
##       Lift: aps, hse, 10-litre, 13a, 14,940, 1760, 1867 
##       Score: safety, registration, register, electoral, indicated, registered, hse 
## Topic 3 Top Words:
##       Highest Prob: make, sure, statement, progress, difference, northern, ireland 
##       FREX: statement, make, sure, progress, ireland, representations, difference 
##       Lift: 101269, 101548, 102938, 103414, 106583, 107305, 109413 
##       Score: make, statement, progress, sure, ireland, northern, milton 
## Topic 4 Top Words:
##       Highest Prob: debt, water, credit, charges, pay, loan, loans 
##       FREX: payday, loan, lenders, debts, loans, debt, charges 
##       Lift: 1,021, 1,025, 1,106, 1,189, 1,273, 1,385, 1,413 
##       Score: debt, water, payday, loan, loans, lenders, credit 
## Topic 5 Top Words:
##       Highest Prob: house, committee, parliament, leader, select, motion, parliamentary 
##       FREX: select, leader, house, motion, committee, backbench, scrutiny 
##       Lift: e-petitions, praying-against, sherlock, wednesdays, sittings, thursdays, 10,000-signature 
##       Score: committee, house, leader, select, scrutiny, parliament, motion 
## Topic 6 Top Words:
##       Highest Prob: new, development, work, need, investment, strategy, must 
##       FREX: development, strategy, develop, project, regional, projects, partnership 
##       Lift: #1,150, 1.245, 1.875, 128406, 131,000, 18-the, 2002-around 
##       Score: development, regional, investment, strategy, infrastructure, projects, work 
## Topic 7 Top Words:
##       Highest Prob: road, petition, residents, car, vehicles, petitioners, dogs 
##       FREX: petitioners, lap-dancing, petition, dogs, dog, pedestrians, cycling 
##       Lift: 0.037, 0.044, 0fficial, 1,042, 1,072, 1,108, 1,122 
##       Score: petitioners, petition, dogs, road, residents, dog, declares 
## Topic 8 Top Words:
##       Highest Prob: important, agree, welcome, country, making, particularly, thank 
##       FREX: agree, welcome, important, absolutely, makes, making, friend's 
##       Lift: adequacies, and-importantly-much, ayse, ballantine, bobtail-and, bostrom, bucketfuls 
##       Score: agree, important, thank, welcome, friend's, absolutely, country 
## Topic 9 Top Words:
##       Highest Prob: companies, market, company, competition, energy, consumers, prices 
##       FREX: competition, companies, market, wholesale, suppliers, company, regulator 
##       Lift: 1,105, 1,345, ashington, boakye, nord, over-charging, price-fixing 
##       Score: companies, consumers, energy, market, company, prices, competition 
## Topic 10 Top Words:
##       Highest Prob: women, men, equality, women's, discrimination, rights, gender 
##       FREX: gender, bishops, transgender, women's, women, abortion, same-sex 
##       Lift: balmforth, bpas, celebrants, cohabitants, jessy, msafiri, natal 
##       Score: women, women's, equality, men, gender, discrimination, marriage 
## Topic 11 Top Words:
##       Highest Prob: energy, climate, fuel, change, green, carbon, emissions 
##       FREX: renewables, solar, insulation, feed-in, biofuels, greenhouse, dioxide 
##       Lift: fossil, #2,500, #solar, 1-are, 1,129, 1,214, 1,343 
##       Score: energy, fuel, carbon, emissions, climate, renewable, renewables 
## Topic 12 Top Words:
##       Highest Prob: office, post, offices, royal, service, closure, mail 
##       FREX: offices, mail, sub-post, post, sub-postmasters, closures, consignia 
##       Lift: #1.8, #210, #450, 1,001, 1,352, 1,639, 1,827 
##       Score: post, offices, office, mail, closure, postal, sub-post 
## Topic 13 Top Words:
##       Highest Prob: mr, north, south, east, west, spoke, friends 
##       FREX: ealing, spoke, dorset, lothian, ayrshire, glasgow, chris 
##       Lift: argos's, blackford, cairns's, clemency, marxist, no2id, 0.66 
##       Score: mr, east, north, south, west, spoke, birmingham 
## Topic 14 Top Words:
##       Highest Prob: pension, scheme, benefit, pensions, benefits, pensioners, system 
##       FREX: pension, esa, pensions, claimants, retirement, pip, pensioners 
##       Lift: means-testing, #20,000, #400, 0º, 1,052, 1,366, 1,482 
##       Score: pension, pensions, pensioners, allowance, scheme, retirement, credit 
## Topic 15 Top Words:
##       Highest Prob: economy, jobs, economic, growth, unemployment, country, investment 
##       FREX: unemployment, recession, growth, economy, obr, deficit, inflation 
##       Lift: 0.76, 0.83, 1,196, 1,319, 1.04, 1.32, 10-about 
##       Score: economy, jobs, unemployment, growth, economic, recession, chancellor 
## Topic 16 Top Words:
##       Highest Prob: schools, school, education, children, teachers, parents, pupils 
##       FREX: academies, pupil, grammar, schools, pupils, teachers, ofsted 
##       Lift: sen2, 11-plus, leas, pupil, 000-to, 1-regardless, 1,000-pupil 
##       Score: schools, school, teachers, pupils, children, education, parents 
## Topic 17 Top Words:
##       Highest Prob: want, say, one, think, know, need, us 
##       FREX: think, say, things, want, something, saying, going 
##       Lift: about-part, arguing-i, beneficial-we, career-many, cause-the, clam, compatriot 
##       Score: think, want, get, say, things, going, us 
## Topic 18 Top Words:
##       Highest Prob: review, report, commission, independent, process, recommendations, inquiry 
##       FREX: recommendations, inquiry, panel, audit, independent, recommendation, reviews 
##       Lift: bourn, cag's, clarke's, clowes, dg, emag, equitable's 
##       Score: fsa, inquiry, review, commission, recommendations, report, independent 
## Topic 19 Top Words:
##       Highest Prob: business, businesses, small, financial, bank, banks, insurance 
##       FREX: smes, medium-sized, businesses, bank, enterprises, enterprise, banking 
##       Lift: 0.21, 0.84, 1,034, 1,130, 10-fold, 1130, 12.19 
##       Score: businesses, business, bank, banks, banking, insurance, small 
## Topic 20 Top Words:
##       Highest Prob: wales, industry, welsh, north-east, england, assembly, constituency 
##       FREX: welsh, wales, steel, cardiff, north-east, assembly, visteon 
##       Lift: a55, angharad, bethesda, co-investment, dewhirst, dogger, gorge 
##       Score: wales, welsh, assembly, manufacturing, steel, north-east, yorkshire 
## Topic 21 Top Words:
##       Highest Prob: care, services, social, mental, need, health, home 
##       FREX: mental, care, social, elderly, older, advocacy, services 
##       Lift: #900,000, 1,051, 1,312, 10,758, 104962, 1089, 13,198 
##       Score: care, mental, services, social, health, older, homes 
## Topic 22 Top Words:
##       Highest Prob: pay, work, workers, employment, working, wage, minimum 
##       FREX: wage, workers, zero-hours, employees, paternity, employer, minimum 
##       Lift: 6.70, 8.20, 85p, awb, e-balloting, hannett, increments 
##       Score: wage, workers, employers, employment, pay, employees, minimum 
## Topic 23 Top Words:
##       Highest Prob: amendment, clause, amendments, new, 1, lords, section 
##       FREX: amendment, nos, insert, subsection, clause, amendments, clauses 
##       Lift: 153a, 22a, 287, 50b, 51b, counter-notice, insured's 
##       Score: clause, amendment, amendments, lords, nos, insert, subsection 
## Topic 24 Top Words:
##       Highest Prob: report, last, since, said, received, published, year 
##       FREX: march, vol, official, january, july, november, published 
##       Lift: 1-2ws, 1,033, 1,099, 1,124,818, 1,337, 1,368,186, 1,595 
##       Score: report, official, vol, published, march, april, november 
## Topic 25 Top Words:
##       Highest Prob: made, clear, impact, decision, changes, recent, assessment 
##       FREX: made, decision, assessment, clear, decisions, impact, implications 
##       Lift: 104963, 107312, 116,400, 125214, 125828, 125830, 126370 
##       Score: made, assessment, impact, changes, decision, decisions, clear 
## Topic 26 Top Words:
##       Highest Prob: funding, cuts, fund, cut, budget, grant, spending 
##       FREX: flood, funding, bbc, formula, grant, flooding, floods 
##       Lift: #10.89, #12, #3.3, #bbcdiversity, 1,027, 1,536-will, 1,546 
##       Score: funding, cuts, flood, bbc, budget, spending, flooding 
## Topic 27 Top Words:
##       Highest Prob: money, spent, extra, spend, liberal, cost, spending 
##       FREX: money, spent, liberal, spend, democrats, tories, tory 
##       Lift: 1-but, 10,309.63, 1228, 158.8, 1763, 18-that, 1979-80 
##       Score: money, liberal, tory, democrats, conservatives, tories, spending 
## Topic 28 Top Words:
##       Highest Prob: constituency, great, community, proud, many, sport, one 
##       FREX: maiden, arts, football, museum, museums, sport, olympic 
##       Lift: 0.27, 0.51, 1,084, 1,126, 1,468, 1,580-plus, 1,983 
##       Score: arts, sport, museum, maiden, heritage, football, constituency 
## Topic 29 Top Words:
##       Highest Prob: families, child, poverty, children, parents, work, credit 
##       FREX: lone, poverty, childcare, families, low-income, child, nursery 
##       Lift: 1,000-discriminates, 1,000-not, 1,080, 1,142,600, 1,170, 1,390, 1,664 
##       Score: poverty, child, families, children, parents, credit, lone 
## Topic 30 Top Words:
##       Highest Prob: party, conservative, vote, parliament, political, election, labour 
##       FREX: party, vote, voting, conservative, party's, voters, election 
##       Lift: alphabetical, gentry, olga, one-party, randomisation, 1,166, 1,294 
##       Score: party, conservative, vote, scottish, election, elections, political 
## Topic 31 Top Words:
##       Highest Prob: point, can, may, issue, take, however, whether 
##       FREX: matter, point, understand, consider, certainly, accept, possible 
##       Lift: 450,000-in, advised-by, backslid, bill-albeit, bizarre-but, can-enable, cases-roughly 
##       Score: point, matter, issue, gentleman's, consider, shall, whether 
## Topic 32 Top Words:
##       Highest Prob: member, said, lady, mentioned, raised, comments, speech 
##       FREX: member, lady, comments, remarks, bromley, interesting, chislehurst 
##       Lift: 12.20, 130b, 1991-forcing, 2,784, 54,000-worth, achieved-is, arguments-and 
##       Score: member, lady, comments, said, speech, raised, points 
## Topic 33 Top Words:
##       Highest Prob: european, uk, eu, countries, united, union, europe 
##       FREX: accession, enlargement, wto, lisbon, treaty, eu, doha 
##       Lift: 13652, 13653, 13654, 1707, balkan, barnier, blackmailing 
##       Score: eu, european, countries, union, treaty, europe, trade 
## Topic 34 Top Words:
##       Highest Prob: education, skills, young, training, students, university, college 
##       FREX: ema, fe, students, apprenticeship, universities, qualifications, apprenticeships 
##       Lift: ema, #ne, 1,188, 1,308, 1,555-what, 1,740, 1,803 
##       Score: students, education, young, skills, apprenticeships, training, universities 
## Topic 35 Top Words:
##       Highest Prob: local, authorities, authority, planning, community, communities, councils 
##       FREX: authorities, local, authority, planning, councils, councillors, locally 
##       Lift: achcew, central-local, laa, lsp, maas, observances, place-shaping 
##       Score: local, authorities, authority, councils, planning, communities, community 
## Topic 36 Top Words:
##       Highest Prob: disabled, carers, disability, support, disabilities, needs, caring 
##       FREX: carers, autism, autistic, disabled, disabilities, disability, dementia 
##       Lift: autism, rnib, #185, #85, #hellomynameis, 1,400-one, 10-person 
##       Score: carers, disabled, disability, autism, disabilities, caring, dementia 
## Topic 37 Top Words:
##       Highest Prob: environment, marine, fishing, sea, industry, natural, fish 
##       FREX: fishermen, cod, forestry, biodiversity, habitats, mmo, fishing 
##       Lift: aquaculture, arable, bee-friendly, biodiversity, birdlife, bycatch, caterpillar 
##       Score: marine, fishing, fishermen, fish, fisheries, wildlife, conservation 
## Topic 38 Top Words:
##       Highest Prob: justice, court, violence, victims, cases, criminal, domestic 
##       FREX: attorney-general, defendants, defendant, prison, prosecutors, solicitor-general, stalking 
##       Lift: #9, 0.08, 0.48, 1,046, 1,237, 10,544, 10.15 
##       Score: violence, prison, court, offence, criminal, rape, victims 
## Topic 39 Top Words:
##       Highest Prob: international, foreign, rights, human, peace, un, conflict 
##       FREX: israel, palestinian, israeli, gaza, sri, zimbabwe, iran 
##       Lift: lankan, saddam, #aleppo, 1,010, 1,476, 1,591, 1224 
##       Score: un, israel, syria, humanitarian, palestinian, israeli, iraq 
## Topic 40 Top Words:
##       Highest Prob: day, family, never, told, families, life, happened 
##       FREX: man, died, son, story, stories, hillsborough, tragedy 
##       Lift: 10,000-seat, 12-inch, 1234, 1519-20, 1635, 1710, 174,995 
##       Score: families, holocaust, family, constituent, man, died, mother 
## Topic 41 Top Words:
##       Highest Prob: proposals, future, forward, consultation, plans, meet, paper 
##       FREX: proposals, consultation, paper, plans, forward, discuss, white 
##       Lift: 10.42, 107910, 109648, 114061, 119621, 141605, 141607 
##       Score: proposals, consultation, plans, future, forward, paper, white 
## Topic 42 Top Words:
##       Highest Prob: behaviour, crime, antisocial, alcohol, young, drugs, drug 
##       FREX: antisocial, asbos, alcohol, alcohol-related, binge, psychoactive, drinking 
##       Lift: acquisitive, addaction, auto, bailes, crawlers, ghb, gilpin 
##       Score: antisocial, crime, behaviour, alcohol, drug, drugs, cannabis 
## Topic 43 Top Words:
##       Highest Prob: housing, homes, social, affordable, private, home, accommodation 
##       FREX: housing, tenants, rented, tenancies, homelessness, leasehold, landlords 
##       Lift: one-for-one, one-bedroom, rented, right-to-buy, #19, #21.5, #28.5 
##       Score: housing, homes, tenants, rented, rent, landlords, affordable 
## Topic 44 Top Words:
##       Highest Prob: question, order, mr, put, asked, answer, questions 
##       FREX: question, answer, questions, speaker, asked, deputy, answers 
##       Lift: 11.00, 11.57, 12.26, 1223, 1232, 1412, 1555-56 
##       Score: question, speaker, mr, answer, deputy, order, questions 
## Topic 45 Top Words:
##       Highest Prob: research, cancer, treatment, medical, condition, screening, disease 
##       FREX: embryos, prostate, cervical, hepatitis, cloning, transplant, embryo 
##       Lift: abnormalities, cystic, embryo, fertilisation, marrow, @cfaware, #500 
##       Score: cancer, patients, embryos, screening, treatment, tissue, breast 
## Topic 46 Top Words:
##       Highest Prob: online, internet, farmers, animals, digital, animal, broadband 
##       FREX: cull, badgers, badger, fur, bovine, mink, culling 
##       Lift: culling, @daisydumble, @donna_smiley, @jimspin, @leamingtonsbc, @maggieannehayes, @nhconsortium 
##       Score: farmers, animals, internet, cull, animal, online, badgers 
## Topic 47 Top Words:
##       Highest Prob: defence, forces, armed, plymouth, personnel, service, military 
##       FREX: mod, naval, hms, submarines, dockyard, veterans, armed 
##       Lift: hms, submarine, submarines, 1,000-people, 1,625, 1,705, 10-3 
##       Score: defence, armed, forces, plymouth, military, personnel, mod 
## Topic 48 Top Words:
##       Highest Prob: information, home, security, data, immigration, control, orders 
##       FREX: extradition, tpims, sia, warrant, detention, checks, tpim 
##       Lift: carlile's, carlile, sia, 10-month, 10,410, 10,500-for, 10.45 
##       Score: immigration, terrorism, detention, terrorist, tpims, home, security 
## Topic 49 Top Words:
##       Highest Prob: police, officers, crime, policing, home, force, service 
##       FREX: constable, constables, officers, policing, police, soca, ipcc 
##       Lift: 2003-morecambe, 9,650, a19s, ashleys, bigg, bounties, ckp 
##       Score: police, officers, policing, crime, forces, constable, neighbourhood 
## Topic 50 Top Words:
##       Highest Prob: nhs, hospital, patients, health, services, hospitals, care 
##       FREX: dentists, dentistry, pharmacies, pct, nhs, hospitals, hospital 
##       Lift: acos, bequest, bernstein, bodmin, catto, cayton, chailey 
##       Score: nhs, patients, hospital, health, patient, hospitals, care 
## Topic 51 Top Words:
##       Highest Prob: tax, budget, cut, chancellor, cuts, rate, income 
##       FREX: 50p, vat, millionaires, hit, tax, allowances, credits 
##       Lift: #840, 0.76p, 1,003, 1,009, 1,226, 1,275, 1,296 
##       Score: tax, vat, budget, credits, chancellor, cuts, income 
## Topic 52 Top Words:
##       Highest Prob: years, now, two, time, first, three, past 
##       FREX: years, three, months, ago, two, past, weeks 
##       Lift: 10-week-old, 10,616, 11-month, 11-point, 1758, 196b, 63,500 
##       Score: years, months, two, ago, three, past, weeks 
## Topic 53 Top Words:
##       Highest Prob: staff, doctors, emergency, medical, service, training, nurses 
##       FREX: ambulance, junior, staffing, doctors, halifax, posts, nurses 
##       Lift: #i'm, 03, 1-who, 1,454, 1,631, 10,000-strong, 10,000-with 
##       Score: staff, doctors, ambulance, nurses, medical, emergency, junior 
## Topic 54 Top Words:
##       Highest Prob: bill, legislation, act, law, rights, provisions, powers 
##       FREX: bill, legislation, bill's, provisions, passage, regulations, legislative 
##       Lift: 1865, 1990s-when, 1998-it, 19may2000, 2003-largely, 2005-have, 29-year 
##       Score: bill, legislation, provisions, rights, law, powers, regulations 
## Topic 55 Top Words:
##       Highest Prob: public, sector, private, organisations, service, voluntary, services 
##       FREX: public, voluntary, organisations, sector, private, co-operative, volunteering 
##       Lift: 1844, af, carpetbaggers, nebulous, puk, 1075, 170-year 
##       Score: public, sector, private, voluntary, organisations, service, services 
## Topic 56 Top Words:
##       Highest Prob: health, national, inequalities, programme, suicide, disease, department 
##       FREX: flu, hiv, pandemic, inequalities, infections, suicide, mortality 
##       Lift: kirkley, lowestoft, nihr, acupuncture, influenza, #148, #3.6 
##       Score: health, vaccine, flu, inequalities, hiv, infection, suicide 
## Topic 57 Top Words:
##       Highest Prob: council, london, areas, city, area, constituency, centre 
##       FREX: county, mayor, borough, cities, liverpool, city, regeneration 
##       Lift: #12,000, #14.4, #356, #38, #5,000, #500,000, #66.6 
##       Score: london, council, city, regeneration, county, rural, borough 
## Topic 58 Top Words:
##       Highest Prob: advice, legal, cases, civil, hull, aid, case 
##       FREX: hull, tribunal, legal, compensation, solicitors, advice, concentrix 
##       Lift: 0300, 1,997, 1.148, 1.4m, 112.8, 1147, 128,687 
##       Score: legal, advice, hull, aid, compensation, civil, tribunal 
## Topic 59 Top Words:
##       Highest Prob: people, work, many, young, get, people's, can 
##       FREX: people, people's, get, getting, work, young, jobcentre 
##       Lift: 2,425, 294,488, 3,699, 37,290, 5,320, 50-to-64, 75589 
##       Score: people, young, work, get, youth, many, people's 
## Topic 60 Top Words:
##       Highest Prob: tax, revenue, relief, duty, uk, avoidance, hmrc 
##       FREX: evasion, hmrc, gaar, avoidance, inland, stamp, revenue 
##       Lift: 1,643, 3.12, 32.2, 44a, 80g, aaronson, aat 
##       Score: tax, hmrc, avoidance, revenue, relief, evasion, territories 
## Topic 61 Top Words:
##       Highest Prob: government, government's, policy, labour, previous, scotland, scottish 
##       FREX: government, previous, policy, government's, scotland, coalition, scottish 
##       Lift: 2005-perhaps, 80994, actually-that, agency-when, agenda-access, alloway, aware-in 
##       Score: government, scotland, scottish, labour, policy, government's, previous 
## Topic 62 Top Words:
##       Highest Prob: trafficking, home, uk, asylum, refugees, immigration, country 
##       FREX: trafficking, slavery, trafficked, sierra, leone, slave, dubs 
##       Lift: #7, 0.025, 1-yes, 1,060, 1,483, 1,746, 1.123 
##       Score: trafficking, refugees, asylum, slavery, trafficked, immigration, sierra 
## Topic 63 Top Words:
##       Highest Prob: food, products, industry, smoking, advertising, tobacco, ban 
##       FREX: gambling, betting, sunbed, tobacco, cocoa, meat, supermarkets 
##       Lift: 0.7p, 00, 0157, 1,000-almost, 1,032, 1,200-i, 1,666 
##       Score: food, smoking, products, tobacco, advertising, gambling, industry 
## Topic 64 Top Words:
##       Highest Prob: members, debate, many, issues, also, today, heard 
##       FREX: members, debate, heard, speak, sides, issues, hear 
##       Lift: noakes, 6.47pm, accept-or, analysis-but, aspect-listening-is, bunfight, called-making 
##       Score: members, debate, issues, many, opposition, heard, constituents 
## Topic 65 Top Words:
##       Highest Prob: children, child, parents, young, children's, family, contact 
##       FREX: csa, adopters, adoption, child's, cafcass, looked-after, children's 
##       Lift: csa, @mandatenow, 10-month-old, 10-went, 12j, 150765, 16-only 
##       Score: children, child, parents, young, children's, adoption, child's 
## Topic 66 Top Words:
##       Highest Prob: transport, rail, bus, services, line, travel, train 
##       FREX: rail, passengers, passenger, heathrow, hs2, freight, high-speed 
##       Lift: 12-car, 15.15, 50.1, adtranz, anti-icing, bahn, chilterns 
##       Score: rail, transport, bus, passengers, fares, trains, hs2 
## Topic 67 Top Words:
##       Highest Prob: year, million, number, increase, figures, increased, billion 
##       FREX: million, figures, figure, increased, increase, compared, year 
##       Lift: #112, #3,850, #84.3, #87.2, 1,249, 102269, 122.9 
##       Score: million, year, billion, increase, figures, average, increased 
## Topic 68 Top Words:
##       Highest Prob: support, ensure, can, help, aware, taking, take 
##       FREX: aware, ensure, support, taking, steps, continue, help 
##       Lift: 103684, 103965, 107320, 111532, 112584, 113698, 117890 
##       Score: support, ensure, steps, aware, help, taking, department 
## Topic 69 Top Words:
##       Highest Prob: deal, recently, new, can, lack, great, concern 
##       FREX: deal, recently, lack, elsewhere, concern, great, improved 
##       Lift: 2004-a, 721,000, added-gva-per, age-of, centres-jacs-which, employment-can, index-the 
##       Score: deal, recently, new, worktrack, lack, can, great

2.2 Full topic model estimate summary - K69

## 
## Call:
## estimateEffect(formula = 1:69 ~ short_list, stmobj = topic_model2, 
##     metadata = lab_corpus_fem_stm$meta, uncertainty = "Global")
## 
## 
## Topic 1:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0170490  0.0002872   59.37 <0.0000000000000002 ***
## short_listTRUE 0.0068967  0.0003515   19.62 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 2:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0058952  0.0002397  24.596 <0.0000000000000002 ***
## short_listTRUE 0.0007065  0.0003026   2.335              0.0196 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 3:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0087151  0.0001117  78.033 <0.0000000000000002 ***
## short_listTRUE 0.0002122  0.0001471   1.442               0.149    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 4:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0064766  0.0003078  21.041 <0.0000000000000002 ***
## short_listTRUE 0.0036170  0.0003905   9.262 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 5:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0120578  0.0002548   47.32 <0.0000000000000002 ***
## short_listTRUE 0.0035458  0.0003329   10.65 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 6:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0313826  0.0004269   73.50 <0.0000000000000002 ***
## short_listTRUE -0.0093819  0.0004914  -19.09 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 7:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0082027  0.0003883  21.126 < 0.0000000000000002 ***
## short_listTRUE 0.0024224  0.0004804   5.043           0.00000046 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 8:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0155991  0.0001353 115.293 < 0.0000000000000002 ***
## short_listTRUE -0.0006691  0.0001476  -4.532           0.00000585 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 9:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0106917  0.0002678  39.930 < 0.0000000000000002 ***
## short_listTRUE -0.0025223  0.0003462  -7.285    0.000000000000325 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 10:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0147928  0.0004467   33.12 <0.0000000000000002 ***
## short_listTRUE 0.0002684  0.0005480    0.49               0.624    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 11:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0131074  0.0004309  30.419 <0.0000000000000002 ***
## short_listTRUE -0.0008988  0.0005145  -1.747              0.0806 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 12:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0100377  0.0003281  30.597 < 0.0000000000000002 ***
## short_listTRUE -0.0026488  0.0003772  -7.023     0.00000000000219 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 13:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0084694  0.0002244  37.745 < 0.0000000000000002 ***
## short_listTRUE 0.0015081  0.0002907   5.189          0.000000212 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 14:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0133791  0.0004686   28.55 <0.0000000000000002 ***
## short_listTRUE 0.0060922  0.0005583   10.91 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 15:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0169596  0.0004287  39.556 < 0.0000000000000002 ***
## short_listTRUE 0.0029699  0.0005137   5.782        0.00000000742 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 16:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0135196  0.0005197  26.017 < 0.0000000000000002 ***
## short_listTRUE 0.0032504  0.0006708   4.846           0.00000126 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 17:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0425088  0.0003149 134.990 <0.0000000000000002 ***
## short_listTRUE 0.0008102  0.0003849   2.105              0.0353 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 18:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0268601  0.0004987   53.86 <0.0000000000000002 ***
## short_listTRUE -0.0068300  0.0005996  -11.39 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 19:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0129044  0.0003253  39.673 < 0.0000000000000002 ***
## short_listTRUE -0.0016916  0.0003815  -4.434           0.00000925 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 20:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0086108  0.0003453   24.94 <0.0000000000000002 ***
## short_listTRUE 0.0060656  0.0004412   13.75 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 21:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0124577  0.0003104   40.14 < 0.0000000000000002 ***
## short_listTRUE -0.0011221  0.0003965   -2.83              0.00465 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 22:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0127304  0.0003168   40.18 <0.0000000000000002 ***
## short_listTRUE 0.0008321  0.0004019    2.07              0.0384 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 23:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0185865  0.0005606  33.153 < 0.0000000000000002 ***
## short_listTRUE -0.0028071  0.0006255  -4.488            0.0000072 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 24:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0138634  0.0002294   60.42 < 0.0000000000000002 ***
## short_listTRUE 0.0013380  0.0002841    4.71           0.00000248 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 25:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0111247  0.0001346  82.629 < 0.0000000000000002 ***
## short_listTRUE 0.0007898  0.0001681   4.697           0.00000265 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 26:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0098005  0.0002865  34.206 <0.0000000000000002 ***
## short_listTRUE 0.0037734  0.0003825   9.864 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 27:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0103148  0.0002170   47.54 < 0.0000000000000002 ***
## short_listTRUE 0.0009108  0.0002727    3.34             0.000839 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 28:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0111687  0.0004059  27.517 < 0.0000000000000002 ***
## short_listTRUE 0.0027939  0.0005148   5.428         0.0000000573 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 29:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0105119  0.0003580  29.363 <0.0000000000000002 ***
## short_listTRUE 0.0042201  0.0004476   9.428 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 30:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0169375  0.0004454  38.029 <0.0000000000000002 ***
## short_listTRUE 0.0008002  0.0005608   1.427               0.154    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 31:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0369135  0.0002231  165.46 <0.0000000000000002 ***
## short_listTRUE -0.0068067  0.0002839  -23.98 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 32:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0100393  0.0001334  75.244 <0.0000000000000002 ***
## short_listTRUE -0.0002580  0.0001609  -1.603               0.109    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 33:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0230342  0.0005126   44.94 <0.0000000000000002 ***
## short_listTRUE -0.0059588  0.0006435   -9.26 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 34:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0136733  0.0004434  30.837 <0.0000000000000002 ***
## short_listTRUE 0.0011096  0.0005518   2.011              0.0444 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 35:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0198252  0.0003413   58.08 <0.0000000000000002 ***
## short_listTRUE -0.0061363  0.0003960  -15.49 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 36:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0061227  0.0003190  19.192 <0.0000000000000002 ***
## short_listTRUE 0.0041027  0.0004135   9.922 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 37:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0069696  0.0003209  21.717 < 0.0000000000000002 ***
## short_listTRUE 0.0014975  0.0004234   3.537             0.000405 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 38:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0226478  0.0005217   43.41 <0.0000000000000002 ***
## short_listTRUE -0.0073624  0.0005950  -12.38 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 39:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0205562  0.0006323  32.510 <0.0000000000000002 ***
## short_listTRUE 0.0011502  0.0008248   1.395               0.163    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 40:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0139283  0.0003594   38.75 <0.0000000000000002 ***
## short_listTRUE 0.0047452  0.0004551   10.43 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 41:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0107945  0.0001176   91.78 <0.0000000000000002 ***
## short_listTRUE -0.0014811  0.0001339  -11.06 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 42:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0150504  0.0004387  34.310 < 0.0000000000000002 ***
## short_listTRUE -0.0033592  0.0005544  -6.059        0.00000000138 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 43:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0140265  0.0004861  28.854 < 0.0000000000000002 ***
## short_listTRUE 0.0021683  0.0006085   3.563             0.000367 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 44:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0147186  0.0002651  55.512 < 0.0000000000000002 ***
## short_listTRUE 0.0016385  0.0003339   4.907          0.000000928 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 45:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0135442  0.0004527  29.917 < 0.0000000000000002 ***
## short_listTRUE -0.0036869  0.0005342  -6.902     0.00000000000517 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 46:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0067855  0.0003691  18.385 <0.0000000000000002 ***
## short_listTRUE 0.0042941  0.0004461   9.625 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 47:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0077717  0.0003512  22.131 < 0.0000000000000002 ***
## short_listTRUE 0.0027458  0.0004467   6.147       0.000000000793 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 48:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0225105  0.0005032   44.74 <0.0000000000000002 ***
## short_listTRUE -0.0077244  0.0005804  -13.31 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 49:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0155733  0.0004778  32.595 < 0.0000000000000002 ***
## short_listTRUE -0.0038787  0.0005826  -6.657       0.000000000028 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 50:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0232451  0.0006510   35.71 <0.0000000000000002 ***
## short_listTRUE -0.0076606  0.0007433  -10.31 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 51:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0105758  0.0003557   29.73 <0.0000000000000002 ***
## short_listTRUE 0.0055875  0.0004505   12.40 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 52:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.01749027 0.00018333  95.403 <0.0000000000000002 ***
## short_listTRUE 0.00001783 0.00022567   0.079               0.937    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 53:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0107597  0.0003954  27.214 <0.0000000000000002 ***
## short_listTRUE 0.0003058  0.0004669   0.655               0.513    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 54:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0261021  0.0004258  61.303 <0.0000000000000002 ***
## short_listTRUE -0.0044960  0.0004877  -9.219 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 55:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0101880  0.0001862  54.730 <0.0000000000000002 ***
## short_listTRUE -0.0004807  0.0002428  -1.979              0.0478 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 56:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0114028  0.0003551  32.109 < 0.0000000000000002 ***
## short_listTRUE -0.0015309  0.0004383  -3.493             0.000479 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 57:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0129042  0.0003259  39.591 <0.0000000000000002 ***
## short_listTRUE 0.0012948  0.0004148   3.121              0.0018 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 58:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0090822  0.0002441  37.205 < 0.0000000000000002 ***
## short_listTRUE 0.0015927  0.0003317   4.801           0.00000158 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 59:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0166452  0.0001943   85.65 < 0.0000000000000002 ***
## short_listTRUE 0.0007286  0.0002343    3.11              0.00187 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 60:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0100794  0.0003770  26.735 <0.0000000000000002 ***
## short_listTRUE -0.0002398  0.0004647  -0.516               0.606    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 61:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0112640  0.0001145   98.35 <0.0000000000000002 ***
## short_listTRUE 0.0019036  0.0001402   13.58 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 62:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0104597  0.0003952  26.468 <0.0000000000000002 ***
## short_listTRUE -0.0010403  0.0004701  -2.213              0.0269 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 63:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0088016  0.0003625  24.283 < 0.0000000000000002 ***
## short_listTRUE 0.0020139  0.0004635   4.345             0.000014 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 64:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0207819  0.0002268  91.627 < 0.0000000000000002 ***
## short_listTRUE 0.0017483  0.0002992   5.844        0.00000000513 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 65:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0136872  0.0003768  36.325 < 0.0000000000000002 ***
## short_listTRUE -0.0017374  0.0004535  -3.831             0.000128 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 66:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0132110  0.0004840  27.295 < 0.0000000000000002 ***
## short_listTRUE 0.0037754  0.0005862   6.441        0.00000000012 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 67:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0219834  0.0003086  71.241 < 0.0000000000000002 ***
## short_listTRUE -0.0011517  0.0003885  -2.965              0.00303 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 68:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0193971  0.0001786  108.61 <0.0000000000000002 ***
## short_listTRUE -0.0026874  0.0002288  -11.75 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 69:
## 
## Coefficients:
##                   Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)     0.00275112  0.00002015 136.555 <0.0000000000000002 ***
## short_listTRUE -0.00002558  0.00002598  -0.985               0.325    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

2.3 Full topic model summary - K30

## A topic model with 30 topics, 81607 documents and a 115477 word dictionary.

## Topic 1 Top Words:
##       Highest Prob: bill, amendment, clause, new, legislation, amendments, act 
##       FREX: amendment, clause, amendments, clauses, nos, insert, subsection 
##       Lift: #185, #85, 0.003, 05, 1-competences, 1-impact, 1,924 
##       Score: clause, amendment, amendments, bill, provisions, lords, nos 
## Topic 2 Top Words:
##       Highest Prob: issues, public, information, also, report, review, process 
##       FREX: consultation, review, guidance, recommendations, information, considering, decisions 
##       Lift: 1-who, 1,842, 109648, 1402, 151387, 1981-was, 1a-has 
##       Score: consultation, guidance, information, review, committee, issues, process 
## Topic 3 Top Words:
##       Highest Prob: women, men, pay, equality, rights, women's, discrimination 
##       FREX: women, equality, gender, equalities, bishops, discrimination, female 
##       Lift: gender, #112, #neverthelesshepersisted, 1-breast-feed, 1,087, 1,574, 1.57 
##       Score: women, women's, equality, men, gender, discrimination, girls 
## Topic 4 Top Words:
##       Highest Prob: police, crime, officers, behaviour, policing, home, antisocial 
##       FREX: policing, antisocial, constable, burglary, wardens, crime, constabulary 
##       Lift: 1,113, 1.24, 17,614, acpo's, adz, alcohol-free, alleygator 
##       Score: police, crime, officers, policing, antisocial, behaviour, constable 
## Topic 5 Top Words:
##       Highest Prob: european, uk, countries, eu, union, trade, international 
##       FREX: treaty, enlargement, wto, lisbon, doha, eu, eu's 
##       Lift: #420, 0.26, 0.56, 07, 09, 1-2, 1-of 
##       Score: eu, european, countries, treaty, armed, defence, forces 
## Topic 6 Top Words:
##       Highest Prob: transport, london, rail, bus, road, services, line 
##       FREX: rail, bus, passengers, fares, trains, buses, passenger 
##       Lift: #145, 0.1p, 0.45, 0.86, 1-very, 1,122, 1,658 
##       Score: rail, transport, bus, passengers, fares, trains, congestion 
## Topic 7 Top Words:
##       Highest Prob: people, work, benefit, pension, benefits, support, disabled 
##       FREX: disabled, jobcentre, incapacity, carers, pension, claimants, esa 
##       Lift: dla, #400, 0300, 1-to-1, 1,030, 1,052, 1,366 
##       Score: pension, carers, disabled, pensions, allowance, disability, credit 
## Topic 8 Top Words:
##       Highest Prob: immigration, safety, uk, asylum, enforcement, home, number 
##       FREX: dogs, dog, id, visa, fur, mink, hse 
##       Lift: 44a, a8, acoba, arcs, attachment-free, bareboat, bonfires 
##       Score: immigration, asylum, animals, dogs, fireworks, dog, animal 
## Topic 9 Top Words:
##       Highest Prob: health, research, cancer, treatment, medical, disease, can 
##       FREX: cancer, diseases, vaccine, flu, embryos, infections, diabetes 
##       Lift: 1169, 20-fold, ablation, abnormalities, adpkd, aed, anaesthesia 
##       Score: cancer, patients, disease, smoking, health, diagnosis, screening 
## Topic 10 Top Words:
##       Highest Prob: government, labour, conservative, party, opposition, policy, government's 
##       FREX: conservative, liberal, democrats, conservatives, scottish, democrat, scotland 
##       Lift: #nationalistsconfused, 1-but, 1.135, 10,182, 10.91, 1125, 116385 
##       Score: conservative, scottish, party, labour, government, scotland, liberal 
## Topic 11 Top Words:
##       Highest Prob: care, health, nhs, services, service, hospital, patients 
##       FREX: dentists, ambulance, dentistry, helier, dentist, nurses, hospital 
##       Lift: 2.24, 2005-6, 22,600, 422, 5.45pm, 8.03, 8.41 
##       Score: nhs, patients, care, hospital, health, patient, hospitals 
## Topic 12 Top Words:
##       Highest Prob: member, members, debate, house, mr, committee, said 
##       FREX: member, speaker, mr, debate, spoke, thoughtful, backbench 
##       Lift: e-petitions, @daisydumble, @percyblakeney63, 10,000-signature, 1028, 1080, 11.00 
##       Score: member, mr, committee, members, speaker, debate, house 
## Topic 13 Top Words:
##       Highest Prob: companies, financial, company, market, scheme, money, debt 
##       FREX: payday, annuity, oft, policyholders, penrose, fca, loan 
##       Lift: fca, oft, prepayment, #1.8, #20,000, 0.21, 0.84 
##       Score: companies, consumers, fsa, banks, company, customers, consumer 
## Topic 14 Top Words:
##       Highest Prob: young, people, health, mental, youth, prison, problems 
##       FREX: prisons, probation, cannabis, reoffending, mental, prison, self-harm 
##       Lift: cannabis, hawton, poppers, camhs, inmates, reoffending, #230 
##       Score: young, mental, prison, drugs, alcohol, youth, drug 
## Topic 15 Top Words:
##       Highest Prob: cases, court, legal, law, case, justice, evidence 
##       FREX: judicial, attorney-general, defendant, extradition, tpims, suspects, court 
##       Lift: 110-day, abscond, absconded, acquittals, adduce, anti-viral, babar 
##       Score: court, offence, courts, criminal, justice, prosecution, offences 
## Topic 16 Top Words:
##       Highest Prob: energy, businesses, business, jobs, investment, economy, industry 
##       FREX: carbon, renewable, renewables, solar, low-carbon, energy, feed-in 
##       Lift: fossil, sellafield, viyella, energy-intensive, low-carbon, #12.5, #140,000 
##       Score: energy, businesses, jobs, economy, manufacturing, industry, investment 
## Topic 17 Top Words:
##       Highest Prob: people, want, one, get, know, say, us 
##       FREX: things, think, something, get, want, going, really 
##       Lift: 1,027, 2.85, 30s-will, 6.37, 778, about-part, accept-there 
##       Score: people, get, think, things, going, want, say 
## Topic 18 Top Words:
##       Highest Prob: education, schools, school, children, training, skills, parents 
##       FREX: schools, teachers, pupils, curriculum, sen, academies, ofsted 
##       Lift: ema, #8,000, 1,000-pupil, 1,051, 1,100-i, 1,170, 1,204 
##       Score: schools, school, education, children, teachers, pupils, students 
## Topic 19 Top Words:
##       Highest Prob: constituency, city, people, many, years, work, centre 
##       FREX: fishermen, cod, hull, plymouth, maiden, fishing, fish 
##       Lift: #14.4, #66.6, 0.27, 0.51, 1,084, 1,126, 1.41 
##       Score: plymouth, constituency, hull, city, fishing, fish, arts 
## Topic 20 Top Words:
##       Highest Prob: housing, homes, people, private, london, social, home 
##       FREX: rent, tenants, landlords, rented, homelessness, homeless, rents 
##       Lift: right-to-buy, #19, #21.5, #28.5, 1,000-odd, 1,026, 1,083 
##       Score: housing, homes, rented, rent, tenants, landlords, affordable 
## Topic 21 Top Words:
##       Highest Prob: tax, year, million, government, budget, cuts, cut 
##       FREX: tax, obr, vat, millionaires, 50p, inflation, budget 
##       Lift: 0.38, 1,869, 107,500, 11.2, 13,600, 2,073, 2.33 
##       Score: tax, cuts, budget, poverty, chancellor, unemployment, billion 
## Topic 22 Top Words:
##       Highest Prob: food, post, office, rural, petition, offices, farmers 
##       FREX: petition, farmers, petitioners, meat, cull, labelling, cattle 
##       Lift: #450, 1072, 11,900, 12-point, 934, a690, ablewell 
##       Score: food, farmers, petitioners, petition, post, rural, offices 
## Topic 23 Top Words:
##       Highest Prob: people, international, human, government, war, rights, country 
##       FREX: syria, israel, civilians, palestinian, israeli, gaza, sri 
##       Lift: muslims, #aleppo, #no2lgbthate, 0.002, 1,000-almost, 1,010, 1,019 
##       Score: syria, un, israel, humanitarian, iraq, palestinian, israeli 
## Topic 24 Top Words:
##       Highest Prob: bbc, media, online, internet, sport, access, digital 
##       FREX: bbc, games, olympic, gambling, bbc's, copyright, lap-dancing 
##       Lift: age-restricted, age-verification, aquatics, bacta, bandwidth, bbfc, bduk 
##       Score: bbc, sport, tickets, internet, digital, online, football 
## Topic 25 Top Words:
##       Highest Prob: local, authorities, funding, areas, services, council, community 
##       FREX: local, authorities, funding, councils, grant, authority, formula 
##       Lift: 416,000, 596,000, 82-3, 885, allison's, baccy, bellwin 
##       Score: local, authorities, funding, councils, authority, council, services 
## Topic 26 Top Words:
##       Highest Prob: children, child, families, care, family, parents, violence 
##       FREX: trafficked, csa, same-sex, adopters, child, rape, marriages 
##       Lift: @mandatenow, 1-regardless, 1,000-discriminates, 1,142,600, 1,483, 1,746, 10-month-old 
##       Score: child, children, parents, violence, care, sexual, rape 
## Topic 27 Top Words:
##       Highest Prob: planning, water, development, land, environment, site, sites 
##       FREX: forestry, biodiversity, masts, habitats, gypsy, flood, waterways 
##       Lift: biodiversity, encampments, masts, #tartantories, 0fficial, 1,000-year-old, 1,251 
##       Score: planning, land, flood, marine, sites, water, site 
## Topic 28 Top Words:
##       Highest Prob: secretary, state, house, last, statement, report, said 
##       FREX: secretary, statement, state, confirm, official, answer, vol 
##       Lift: 12.40, ashleys, ayia, burne, cabinet's, cairns's, clutha 
##       Score: secretary, state, statement, answer, confirm, inquiry, leader 
## Topic 29 Top Words:
##       Highest Prob: parliament, wales, vote, commission, political, assembly, people 
##       FREX: electoral, polling, gibraltar, voting, assembly, vote, votes 
##       Lift: @leamingtonsbc, @maggieannehayes, @nhconsortium, #keeptweeting, 1-46, 1-would, 1,294 
##       Score: electoral, vote, elections, wales, assembly, referendum, welsh 
## Topic 30 Top Words:
##       Highest Prob: can, make, ensure, agree, important, take, made 
##       FREX: agree, aware, sure, ensure, taking, lady, welcome 
##       Lift: 1565, 19602, 2,095, 42931, 94254, agencies-an, anguish-filled 
##       Score: agree, aware, thank, ensure, point, lady, can

2.4 Full topic model estimate summary - K30

## 
## Call:
## estimateEffect(formula = 1:30 ~ short_list, stmobj = topic_model_k30, 
##     metadata = lab_corpus_fem_stm$meta, uncertainty = "Global")
## 
## 
## Topic 1:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0449702  0.0006448  69.739 <0.0000000000000002 ***
## short_listTRUE -0.0068689  0.0008156  -8.422 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 2:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0757500  0.0006134  123.48 <0.0000000000000002 ***
## short_listTRUE -0.0215054  0.0007219  -29.79 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 3:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0230229  0.0006035  38.149 <0.0000000000000002 ***
## short_listTRUE -0.0007421  0.0006919  -1.073               0.283    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 4:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0289385  0.0006492  44.578 <0.0000000000000002 ***
## short_listTRUE -0.0074447  0.0007523  -9.896 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 5:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0368452  0.0006689  55.085 < 0.0000000000000002 ***
## short_listTRUE -0.0051017  0.0007838  -6.509      0.0000000000759 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 6:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0217916  0.0005761  37.829 < 0.0000000000000002 ***
## short_listTRUE 0.0054441  0.0007613   7.151    0.000000000000868 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 7:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0298344  0.0006489   45.97 <0.0000000000000002 ***
## short_listTRUE 0.0131773  0.0007897   16.69 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 8:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0186028  0.0004845  38.398 <0.0000000000000002 ***
## short_listTRUE 0.0004560  0.0006261   0.728               0.466    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 9:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0249526  0.0005936  42.033 < 0.0000000000000002 ***
## short_listTRUE -0.0051042  0.0007216  -7.073     0.00000000000153 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 10:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0378866  0.0004700  80.618 < 0.0000000000000002 ***
## short_listTRUE 0.0019999  0.0005925   3.375             0.000738 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 11:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0408025  0.0006947  58.736 <0.0000000000000002 ***
## short_listTRUE -0.0083331  0.0008785  -9.486 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 12:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0390032  0.0006049   64.47 <0.0000000000000002 ***
## short_listTRUE 0.0082105  0.0007367   11.14 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 13:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0302322  0.0006073   49.78 < 0.0000000000000002 ***
## short_listTRUE -0.0028580  0.0007663   -3.73             0.000192 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 14:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0239348  0.0005284  45.296 < 0.0000000000000002 ***
## short_listTRUE -0.0032166  0.0006376  -5.045          0.000000456 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 15:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0409453  0.0006981   58.66 <0.0000000000000002 ***
## short_listTRUE -0.0167479  0.0008565  -19.55 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 16:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0384177  0.0006692  57.406 < 0.0000000000000002 ***
## short_listTRUE -0.0046120  0.0008693  -5.305          0.000000113 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 17:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0788485  0.0005733 137.533 < 0.0000000000000002 ***
## short_listTRUE 0.0019968  0.0007196   2.775              0.00552 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 18:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0298568  0.0006882  43.381 < 0.0000000000000002 ***
## short_listTRUE 0.0037965  0.0008478   4.478           0.00000755 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 19:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0196088  0.0005073   38.65 <0.0000000000000002 ***
## short_listTRUE 0.0092533  0.0006687   13.84 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 20:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0204491  0.0005531  36.970 < 0.0000000000000002 ***
## short_listTRUE 0.0038428  0.0007524   5.107          0.000000328 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 21:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0460876  0.0008011   57.53 <0.0000000000000002 ***
## short_listTRUE 0.0142956  0.0010103   14.15 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 22:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0139586  0.0004706  29.661 <0.0000000000000002 ***
## short_listTRUE 0.0055231  0.0005725   9.647 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 23:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0261614  0.0006770  38.645 <0.0000000000000002 ***
## short_listTRUE 0.0007679  0.0008461   0.908               0.364    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 24:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0124067  0.0003947  31.436 <0.0000000000000002 ***
## short_listTRUE 0.0049008  0.0004951   9.899 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 25:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0426172  0.0006188   68.88 <0.0000000000000002 ***
## short_listTRUE -0.0085936  0.0007492  -11.47 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 26:
## 
## Coefficients:
##                   Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)     0.02501676  0.00056573  44.220 <0.0000000000000002 ***
## short_listTRUE -0.00003169  0.00073090  -0.043               0.965    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 27:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0149391  0.0004739  31.523 <0.0000000000000002 ***
## short_listTRUE 0.0003263  0.0005921   0.551               0.582    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 28:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0427024  0.0005845   73.06 <0.0000000000000002 ***
## short_listTRUE 0.0145518  0.0007268   20.02 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 29:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0188018  0.0005255  35.777 <0.0000000000000002 ***
## short_listTRUE 0.0062939  0.0006902   9.118 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 30:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0526689  0.0003202 164.510 <0.0000000000000002 ***
## short_listTRUE -0.0037508  0.0003916  -9.577 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

2.5 AWS References to Constituents in Context

A random selection of 2% of all references to “my constituency”, “my constituent” and “my constituents”, by AWS MPs, in context.